Dynamic Dual-Reinforcement-Learning Routing Strategies for Quality of Experience-aware Wireless Mesh Networking

• The impact of transmission impairments such as loss and latency on user perceived quality(QoE) depends on the service type. In a real network, multiple service types such as audio,video, and data coexist. This makes resource management inherently complex and difficultto orchestrate. In this paper, we propose an autonomous Quality of Experience managementapproach for multiservice wireless mesh networks, where individual mesh nodes apply rein-forcement learning methods to dynamically adjust their routing strategies in order to maxi-mize the user perceived QoE for each flow. Within the forwarding nodes, we develop a novelpacket dropping strategy that takes into account the impact on QoE. Finally, a novel sourcerate adaptation mechanism is designed that takes into account the expected QoE in order tomatch the sending rate with the available network capacity. An evaluation of our mechanismsusing simulations demonstrates that our approach is superior to the standard approaches,AODV and OLSR, and effectively balances the user perceived QoE between the service flows.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

QoE-awareness

Wireless mesh networks

Reinforcement learning

Routing Packet scheduling

Rate adaptation

Computer Science

Datavetenskap

Publikations- och innehållstyp

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